5 Février - 11 Février


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Jeudi 8 Février
Heure: 10:30 - 11:30
Lieu: Salle B107, bâtiment B, Université de Villetaneuse
Résumé: Exponentially large arc-flow models
Description: François Clautiaux Network flow formulations are among the most successful tools to solve optimization problems. Such formulations correspond to determining an optimal flow in a network. One particular class of network flow formulations is the arc flow, where variables represent flows on individual arcs of the network. In this talk, we will review classical and recent results on integer linear programming models based on arc-flow formulations in exponentially or pseudo-polynomial size networks. We will study the limitations of these approaches, and how various almost disconnected groups have addressed these limitations. We will describe a recent approach based on the generalization of these models to flow in hypergraphs, and propose some research directions.
Heure: 12:15 - 13:00
Lieu: Salle B107, bâtiment B, Université de Villetaneuse
Résumé: The role of Knowledge Graphs in externalizing information from conceptual models
Description: Ana-Maria Ghiran Due to the machine readable format used by Knowledge Graphs (KGs) in representing facts, and ontological models, they enabled AI systems to make decisions or to provide humans with insights by revealing hidden relationships between entities. Nevertheless, decision making in enterprises is far from being assigned to AI. Describing and evaluating business processes take the form of visual models that gained increased popularity among managers. But a business process diagram, usually described in the standardized notation BPMN (Business Process Model and Notation), enables more than just a visual representation of the knowledge – it creates a structured encoding of knowledge, which can be captured in a graph-based format. In this way, information that captures diverse facets of an enterprise (e.g. about business processes, resources, strategies, goals etc.) and that was mainly used by business executives and restricted to human interpretation, is externalized as KGs and provided for machine interpretation, thus enabling reasoning and semantic linking with external knowledge. In this presentation I will highlight that conceptual models should be considered as knowledge acquisition structures for any domain and that they can be processed as KGs with the help of Semantic Technology.